Wei Yang1,2,*, Ivy Kim D. Machica1
Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 889-905, 2024, DOI:10.32604/iasc.2024.055133
- 31 October 2024
Abstract Birds play a crucial role in maintaining ecological balance, making bird recognition technology a hot research topic. Traditional recognition methods have not achieved high accuracy in bird identification. This paper proposes an improved ResNet18 model to enhance the recognition rate of local bird species in Yunnan. First, a dataset containing five species of local birds in Yunnan was established: C. amherstiae, T. caboti, Syrmaticus humiae, Polyplectron bicalcaratum, and Pucrasia macrolopha. The improved ResNet18 model was then used to identify these species. This method replaces traditional convolution with depth wise separable convolution and introduces an SE (Squeeze and Excitation) module to More >
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